Airborne relays in cooperative-MIMO systems

ABSTRACT

An Unmanned Aerial Vehicle (UAV) comprises a situational awareness system coupled to at least one onboard sensor and senses the location of other UAVs. A cooperative Radio Access Network (RAN)-signal processor is configured to process RAN signals cooperatively with at least one other UAV to produce RAN performance criteria. A flight controller provides autonomous navigation control of the UAV&#39;s flight based on the relative spatial locations of other UAVs and the RAN performance criteria, which operates within predetermined boundaries of navigation criteria. The UAV can employ mitigation tactics against one or more radio devices identified as a threat and may coordinate other UAVs to conduct such mitigations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/218,609, filed Jul. 25, 2016, now U.S. Pat. No. 9,798,329, whichclaims priority to U.S. Provisional Application 62/197,336, filed Jul.27, 2015, each of which is expressly incorporated by reference in itsentirety.

FIELD OF THE INVENTION

The present invention relates, in general, to control of unmanned aerialvehicles (UAVs), and, more particularly, to control methods and systemsfor use with swarms of UAVs configured to function as relays in a radioaccess network.

BACKGROUND

The background description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

There is a growing interest in utilizing unmanned aerial vehicles(UAVs), such as remotely controlled drones/airplanes, helicopters, andmulticopters, to perform a wide variety of tasks. An ongoing challenge,though, is how to better control the UAVs for each of these particularuses.

In some applications, it is desirable or useful to perform a taskthrough the use of two or more UAVs that are controlled in a centralizedor organized manner. For example, swarm control may be used to controlthe UAVs as they fly over a targeted geographical area. A swarm can bethought of as a self-organizing particle system with numerousautonomous, reflexive agents (e.g., UAVs are the particles) whosecollective movements may be determined by local influences, such as windand obstacles (such as another nearby UAV). The UAVs are independent andare often locally controlled, which may include communicating with anearby UAV to determine which one moves or whether both should move toavoid an impending collision.

Formations of multi-agent systems can be controlled based on two mainapproaches—namely: centralized and decentralized. The centralizedapproach has the merit of global information that each agent can receivethrough a central controller. In the decentralized approach, each agenthas a local controller which increases its reliability. A decentralizedapproach can also be useful when global information is not available.

In the context of flight management, synchronization is agreement intime or simultaneous operation, which is an important concept in thecontrol of dynamical systems. An important technique to improvesynchronization performance is cross coupling, which is based on sharingthe feedback information of control loops, and it has many applicationsin the motion synchronization of multi-axis and cooperative manipulatorrobots. On the one hand, coupling with more agents provides a bettermotion synchronization; on the other hand, communication range of agentsrestricts the agents with whom an agent can be coupled.

The use of UAVs for achieving high-speed wireless communications isexpected to play an important role in future communication systems. In aradio access network (RAN), UAVs equipped with RAN transceivers can beused to quickly deploy RAN services and provide reliable broadbandnetwork infrastructure with lower CAPEX and OPEX compared to terrestrialcellular networks. For example, such UAVs can function as relays betweena ground-based base transceiver station (BTS) and one or more userequipments (UEs).

The high mobility of UAVs can result in highly dynamic networktopologies, which are usually sparsely and intermittently connected. Asa result, effective multi-UAV coordination, or UAV swarm operations,should be designed to ensure reliable network connectivity.

Another challenge stems from the size, weight, and power constraints ofUAVs, which limit their communication, computation, and endurancecapabilities. To address such issues, energy-aware UAV deployment andoperation mechanisms are needed for intelligent energy usage andreplenishment. Moreover, due to the mobility of UAVs, as well as thelack of fixed backhual links, interference coordination amongneighboring cells with UAV-enabled aerial base stations is morechallenging than in terrestrial cellular systems. While conventionalwisdom might call for effective interference management techniques forUAV-aided cellular coverage, Cooperative Multiple-Input Multiple Output(MIMO) has been shown to exploit interference to provide dramaticimprovements to RAN performance.

In 2001, Shattil introduced Coordinated Multipoint (U.S. Prov. Appl. No.60/286,850) with joint MIMO processing between cooperating BTSs. Thisenables universal frequency reuse whereby the full RAN spectrum can beused to serve each and every UE. In 2002, Shattil introduced Cloud-RAN(U.S. Pat. No. 7,430,257) wherein Software Defined Radio (SDR) isimplemented via distributed computing in a Coordinated Multipointsystem. In 2002 and 2004, Shattil described client-side Cooperative MIMO(U.S. Pat Pub. 20080095121 and U.S. Pat. No. 8,670,390). By employingCooperative MIMO at both ends of a RAN link, the total data bandwidthper UE is no longer limited by the RAN spectrum. Rather, it isdetermined by the short-range, high-bandwidth fronthaul network thatconnects cooperating devices. This solves one of the most importantproblems in radio communications. Each of the references mentionedherein is incorporated by reference in its entirety.

In order for UAVs to serve in a Cooperative-MIMO network, for example,the UAVs could be coordinated spatially to ensure and/or enhance RANperformance. UAV control for RAN applications could include providingfor communications between UAVs, providing for synchronization, clusterformation, as well as other control and/or sensing capabilities whichare also essential for swarm management. Hence, there is a need toenable a behavioral structure for swarm management configured formulti-objective missions that include RAN performance enhancement, andcan further comprise flight management, target seeking, obstacleavoidance, as well as others.

SUMMARY

Wireless communication systems that include UAVs promise to provide costeffective wireless connectivity for devices without infrastructurecoverage. Compared to terrestrial communications or those based onhigh-altitude platforms (HAPs), on-demand wireless systems withlow-altitude UAVs are faster to deploy, more flexibly re-configured, andare likely to have better communication channels. Aspects of thedisclosure address many of the challenges of using highly mobile andenergy constrained UAVs for wireless communications.

Aspects of the disclosure describe cooperative-MIMO processing, whichcan comprise MIMO processing wherein the multiple input (MI) comprisesdownlink transmissions from multiple UAVs and the multiple output (MO)comprises uplink transmissions to multiple UAVs. Further aspects of thedisclosure indicate that cooperative-MIMO processing can employ UEs,BTSs, and/or relay nodes, such as UAVs. In accordance with some aspects,combinations of BTSs, UAVs, UEs, and at least one central processor (CP)are configured to perform cooperative-MIMO processing.

In some aspects, a UAV comprises a situational awareness system coupledto at least one onboard sensor and configured to sense the location ofother UAVs. A cooperative Radio Access Network (RAN)-signal processor isconfigured to process RAN signals in a UAV-User Equipment (UE) channelcooperatively with at least one other UAV to produce RAN performancecriteria, the cooperative processing providing for increased rank of theUAV-UE channel. A flight controller is coupled to the situationalawareness system and the cooperative RAN-signal processor, and employsautonomous navigation control of the UAV's flight based on the relativespatial locations of other UAVs and based on the RAN performancecriteria operating within predetermined boundaries of navigationcriteria. As used herein, RAN performance criteria can include RANmitigation performance criteria. In some aspects, the UAV is configuredto employ mitigation tactics against one or more UEs identified as athreat and may coordinate other UAVs to conduct such mitigations. RANperformance criteria can be employed as RAN mitigation criteria, and theflight controller can enhance RAN mitigation performance by adapting theautonomous navigation control. Transmitter and receiver apparatusesconfigured to perform the aforementioned operations can be employed inaspects of the disclosure.

The following patent applications are incorporated by reference in theirentireties: Appl. Nos. 60/286,850, filed Apr. 26, 2001; Ser. No.10/131,163, filed Apr. 24, 2002; Ser. No. 10/145,854, filed May 14,2002; Ser. No. 11/187,107, filed Jul. 22, 2005; Ser. No. 14/168,442,filed Jan. 27, 2015; Ser. No. 14/709,936, filed May 12, 2015,62/233,982, filed Sep. 28, 2015; and 62/252,717 filed Nov. 9, 2015.

BRIEF DESCRIPTION OF THE DRAWING

Flow charts depicting disclosed methods comprise “processing blocks” or“steps” may represent computer software instructions or groups ofinstructions. Alternatively, the processing blocks or steps mayrepresent steps performed by functionally equivalent circuits, such as adigital signal processor or an application specific integrated circuit(ASIC). The flow diagrams do not depict the syntax of any particularprogramming language. Rather, the flow diagrams illustrate thefunctional information one of ordinary skill in the art requires tofabricate circuits or to generate computer software to perform theprocessing required in accordance with the present disclosure. It shouldbe noted that many routine program elements, such as initialization ofloops and variables and the use of temporary variables are not shown. Itwill be appreciated by those of ordinary skill in the art that unlessotherwise indicated herein, the particular sequence of steps describedis illustrative only and can be varied. Unless otherwise stated, thesteps described below are unordered, meaning that the steps can beperformed in any convenient or desirable order.

FIG. 1 is a block diagram of a network topology in accordance with anaspect of the disclosure.

FIG. 2 is a block diagram of a UAV in accordance with certain aspects ofthe disclosure.

FIGS. 3A, 3B, and 3C depict flow diagrams that illustrate methodsaccording to aspects of the disclosure.

FIG. 4 illustrates a procedure that can be implemented by a computerprocessor configured to operate according to aspects of the disclosure.

FIG. 5 illustrates relationships between navigational directives and RANdirectives associated with managing a swarm of UAVs in accordance withaspects of the disclosure.

FIG. 6 is a block diagram of a UAV in accordance with certain aspects ofthe disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described below. It should beapparent that the teachings herein may be embodied in a wide variety offorms and that any specific structure, function, or both being disclosedherein are merely representative. Based on the teachings herein oneskilled in the art should appreciate that an aspect disclosed herein maybe implemented independently of any other aspects and that two or moreof these aspects may be combined in various ways. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth herein. In addition, such an apparatusmay be implemented or such a method may be practiced using otherstructure, functionality, or structure and functionality in addition toor other than one or more of the aspects set forth herein.

Descriptions of various terms (including “fronthaul,” “precoding,” “UE,”“BTS,” “Node,” and “CP”) used throughout the disclosure can be found inU.S. Prov. Appl. No. 62/197,336, filed Jul. 27, 2015, which isincorporated by reference in its entirety.

One of the features of a MIMO system in accordance with some aspects ofthe disclosure is a multipath communication channel comprising multipleactive scattering platforms, such as depicted in FIG. 1 by a pluralityof airborne relay stations (e.g., UAVs) 111.1-111.4, 112.1-112.3, and113.1-113.3. Active scattering platforms can serve to increase the rankof the RAN channel matrix in the forward and/or reverse link betweenBTS(s) and UE(s). As a result, a multipath propagation channel from asource (e.g., one or more BTSs 100.1 and 100.2) to multiple destinations(e.g., UEs 120.1-120.5) will not only be measurable, but alsocontrollable via these active scattering platforms 111.1-111.4,112.1-112.3, and 113.1-113.3 to more efficiently provide frequency reuseamong multiple users 120.1-120.5 via discriminative propagationfeatures. Some aspects of the disclosure provide means for multipleusers to re-use allocated spectrum concurrently in MIMO communicationconfigurations and enable these users to share allocated resourcesdynamically and efficiently.

In a first general aspect, the UEs 120.1-120.5 employ a RAN tocommunicate with one or more BTSs 101.1 and 101.2 via the activescattering platforms 111.1-111.4, 112.1-112.3, and 113.1-113.3. Radiouplinks are indicated by links 141.u, 142.u, and 143.u, and radiodownlinks are indicated by links 141.d, 142.d, and 143.d. However, oneor more of these downlinks may employ optical communications.Communications between the active scattering platforms 111.1-111.4,112.1-112.3, and 113.1-113.3, such as indicated by link 160, cancomprise any of various short-range broadband wireless links, including(but not limited to) WPAN and WLAN radio technologies, and opticalcommunication technologies. Downlinks from the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3 to the BTSs 101.1and 101.2 are denoted by links 151.d, 152.d, and 153.d. Uplinks from theBTSs 101.1 and 101.2 to the active scattering platforms 111.1-111.4,112.1-112.3, and 113.1-113.3 are denoted by links 151.u, 152.u, and153.u. It should be appreciated that the links 151.d, 152.d, 153.d,151.u, 152.u, and 153.0 may comprise optical and/or radio communicationlinks.

Any of the methods and devices described herein may be implemented in anetwork configuration embodied by this first general aspect, as well asin network configurations that are configured in accordance withparticular aspects of the disclosure. While the first general aspect canbe inferred from FIG. 1, additional aspects, such as networkconfigurations that employ HAPs and/or satellites to communicate withthe active scattering platforms may be provided.

In one aspect, the UEs 120.1-120.5 are configured in cooperative groups(e.g., user groups 131.1-131.3) wherein the UEs within each groupcoordinate operations, share resources, or otherwise cooperate toprocess signals in the RAN. In another aspect, the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3 are organized inclusters 132.1-132.3 and may provide for inter-cluster and/orintra-cluster communications. Platforms within each cluster may beconfigured to cooperate with each other, such as to coordinate and/orcollaborate for processing signals communicated with the UEs120.1-120.5, signals exchanged between the clusters 132.1-132.3, and/orsignals exchanged with the BTSs 100.1 and 100.2.

In another aspect, the BTSs 100.1-100.3 are communicatively coupled to aCP 130 configured to perform at least some of the signal-processingoperations required by the BTSs 100.1-100.3, the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3, and/or the UEs120.1-120.5. Such signal processing can be implemented in asoftware-defined radio (SDR) which resides within a cloud computingarchitecture, such as servers in one or more data centers. In someaspects, distributed computing may be effected via devicescommunicatively coupled together throughout the network, such as the UEs120.1-120.5, the active scattering platforms 111.1-111.4, 112.1-112.3,and 113.1-113.3, and/or other network devices.

By employing central processing, a variety of signal processing tasksranging from complex global operations to many types of local operationscan be pooled. This can reduce the cost, power needs, and/or processingrequirements of edge devices, such as BTSs and active scatteringplatforms, relay nodes, etc. In some aspects, certain operations arebetter suited to be performed at the network edges. For example,edge-distributed operations can reduce latency and communicationbandwidth requirements. Various cost estimations and other evaluationalgorithms can be used to determine which operations to perform at theedges and when to perform them. In some aspects, edge processing cancomprise simple operations, but when viewed from a global perspective,exhibit complex collective intelligence. In some aspects of thedisclosure, combinations of edge processing and centralized processingcan be performed to achieve an equitable (e.g., improved) balancebetween simplifying edge devices, achieving adequate responsiveness tochanging conditions, and maintaining manageable fronthaul loads.

Another aspect illustrated in FIG. 1 employs the concept ofdevice-agnostic cooperation. Specifically, devices can be configured tochange their roles, such as to serve multiple purposes. By way ofexample, group 131.2 comprises UE 120.2 and BTS 101.2. In one aspect,the UE 120.2 cooperates with the BTS 101.2 to function as an extra RANantenna(s) for the BTS 101.2 array. The UE 120.2 might employ a wirelessfronthaul link to communicate directly with the BTS 101.2, such as tocoordinate transmission and/or reception in the RAN. Similarly, the BTS101.2 could be configured to assist the UE 120.2, such as by increasingthe channel matrix rank for cooperative subspace processing operationsin the RAN. In another example, one or more of the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3 might form acooperative array with any of the UEs 120.1-120.5, any of the BTSs100.1-100.3, or any combinations thereof. For example, an activescattering platform might join a UE group to assist in cooperative MIMOprocessing of RAN signals for the group. An active scattering platformcould join one or more BTSs to function as a cooperative basetransceiver array. In some aspects, UEs and/or BTSs could join a clusterof active scattering platforms and assist in relaying wireless signalsto another cluster of active scattering platforms.

Also depicted in FIG. 1 is a network wherein the RAN channel is providedby both terrestrial base transceiver communications (such as depicted bylink 144 from BTS 100.3) and active scattering platforms (such as RANlinks 143.0 and 143.d from the active scattering platform group 132.3.In such aspects, cooperative MIMO subspace processing can be performedat the CP 130, which also coordinates the cooperating RAN nodes (e.g.,BTS 100.3 and scattering platform group 132.3) for transmitting precodedRAN signals and/or receiving RAN signals.

While the UEs 120.1-120.5 and the BTSs 100.1-100.3 are depicted havingantenna systems (121.1-121.2 and 101.1-101.3, respectively), other typesof wireless transceivers can be employed. For example, some of the links(such as, but not limited to, 151.u, 151.d, 152.u, 152.d, 153.u, and153.d) may comprise optical links. Thus, LEDs and photodetector arrays(or other suitable optical transceivers) can be employed astransceivers. While each of the antenna systems 121.1-121.2 and101.1-101.3 may comprise one or more antennas, optical transceivers caninclude singular or multiple light transmitters and singular or multiplelight detectors. It should be appreciated that each of the activescattering platforms 111.1-111.4, 112.1-112.3, and 113.1-113.3 comprisessome sort of wireless transceiver apparatus, such as at least oneantenna, and optionally, at least one optical transmitter, and at leastone optical receiver.

In accordance with some aspects of the disclosure, the BTS 100.1-100.2transceiver apparatus 101.1-101.2 can comprise lamps, spotlights,traffic signals, street lights, and/or other types of ubiquitouslighting, wherein such ubiquitous light sources are modulated with datato provide the uplinks 151.u, 152.u, and 153.u. In some aspects, atleast some of the communication links (e.g., uplinks 151.u, 152.u, and153.u) can be multi-purpose in terms of providing additional functionsbesides communications. For example, in one aspect, the uplinks 151.u,152.u, and 153.0 provide power to at least some of the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3. In another aspect,the uplinks 151.u, 152.u, and 153.0 supply synchronization signals (orother control signals) to at least some of the active scatteringplatforms 111.1-111.4, 112.1-112.3, and 113.1-113.3. In another aspect,the uplinks 151.u, 152.u, and 153.0 may provide for locating, tracking,and/or directing the active scattering platforms 111.1-111.4,112.1-112.3, and 113.1-113.3.

In accordance with another aspect, the active scattering platforms111.1-111.4, 112.1-112.3, and 113.1-113.3 may organize themselves intogroups (or clusters) 132.1-132.3 and/or they may be organized into theirrespective groups by ground operations (e.g., the CP 130). In someaspects, each group can have a cluster head (e.g., each of the activescattering platforms 111.1, 112.1, and 113.1 might function as a clusterhead for its respective group 132.1, 132.2, and 132.3). Each clusterhead 111.1, 112.1, and 113.1 can provide services to other members ofits respective group, including (but not limited to) synchronizing theother members, communicating with BTSs on behalf of the group,organizing group operations in accordance with command and controlsignals received from ground units, controlling communications withinthe group, controlling communications between groups, performing centralprocessing operations for the group, coordinating the group tocommunicate via the RAN with the UEs and/or BTSs, determining headingfor the group, organizing the group's flight formation, performingcomputational and/or power load balancing among group members, and/ortransferring control responsibilities to another group member.

In some aspects, an active scattering platform (e.g., 111.4) can belongto more than one group (e.g., 132.1 and 132.2). The platform 111.4 canreceive and process control information from multiple cluster heads(e.g., 111.1 and 112.1). The platform 111.4 may disregard at least someof the commands and control signals from at least one of the multiplecluster heads 111.1 and 112.1. In some aspects, the platform 111.4 mayselect which cluster head to receive control information from. In someaspects, another device (e.g., a cluster head, a BTS, a UE, and/or theCP) determines which cluster head(s) provide which control signals tothe platform 111.4.

In the description that follows, “forward link” describes thecommunication path from the BTS(s) to the UE(s), which can include aforward uplink (e.g., link 151.u), any communication link(s) between theactive scattering platforms (e.g., relay channels, such as link 160),and a forward downlink (e.g., link 143.d) to the UE(s). The forward linkcan also include the communication links between the CP and the BTSs.

The “reverse link” describes the communication path from the UE(s) tothe BTS(s), which can include a reverse uplink (e.g., link 143.u), anycommunication link(s) between the active scattering platforms (e.g.,relay channels, such as link 160), and a reverse downlink (e.g., link143.u) to the BTS(s). The forward link can also include thecommunication links between the BTSs and the CP.

Relay channels may be characterized by what they do to the message froma source node, such as amplify-and-forward (AF), decode-and-forward(DF), or filter-and-forward (FF); each with different challenges interms of the presence of noise, algorithm complexity, and required nodeinformation. The active scattering platforms can provide for amultipath-dominant channel wherein at least some of the multipaths arethrough the active scattering platforms. In some aspects, theseplatforms are in parallel paths providing amplifications, delays, anddirectional adjustments from a source to a destination (e.g., UEs andBTSs). In some aspects, the sources and destinations are on the ground,and the airborne platforms serve as bent-pipe transponders, whichperform receiving, low-noise-amplifying, filtering, frequencytranslating, power-amplifying, and re-radiating functions for servingsignals.

In some aspects, MIMO configurations feature a point-to-pointarchitecture with a source at a communication hub via radiations frommultiple BTSs to a destination in a common coverage of the BTSs, theairborne platforms, or some combination thereof. In some aspects, theMIMO configurations feature a point-to-multipoint architecture with asource at a communication hub via radiations from multiple BTSs tomultiple destinations in a common coverage of the BTSs, the airborneplatforms, or some combination thereof.

In accordance with certain aspects, the MIMO systems utilize compositetransfer functions selected and characterized based on channel stateinformation (CSI), which can comprise responses from probing signalsequences for a propagation channel dominated by multipath. Eachpropagating path features a set of unique transponding function, and thecomposited transfer functions can be constructed or shaped to be “userdependent” with enhanced responses to a selected user and (optionally)suppressed ones for other users. In some aspects, when operating incoordinated modes, cooperating UEs are configured to suppressinterference to other UEs using the same frequency resources as the RANor by communicating information via a local area network to effectbaseband cancellation.

As first described in Applicant's '850 patent application, distributedmulti-user MIMO (MU-MIMO) is a set of advanced MIMO technologies wherethe available antenna elements are spread over multiple independentaccess points or radio terminals, each terminal with one or more antennaelements. To enhance communication capabilities of all terminals,MU-MIMO is provided with an extended version of space-division multipleaccess (SDMA) to allow multiple transmitters to send subspace-codedsignals and multiple receivers to receive separate signalssimultaneously in the same frequency.

In the '163 and '854 applications, distributed multi-user MIMOimplementations include airborne and air-ground communications. Thus,relay nodes can include any combination of airborne and terrestrialcommunication platforms. In some aspects, multiple parallel paths arethrough multiple active bent-pipe transponding platforms. The MIMOchannel transfer functions are based on available CSI, which can includethe aggregate effects of propagating through one or more relay nodes,and spatial multiplexing (e.g., subspace precoding and/or decoding)weights provide for frequency reuse to enable multiple users toconcurrently employ the same spectral resources in a RAN.

In some aspects, the network depicted in FIG. 1 can be configured toperform RAN mitigation. For example, upon detection of an unauthorizedradio signal or determination that a remote-controlled vehicle (e.g., aUAV) may be a threat, one or more of the UEs may be identified as theradio controller of the vehicle. The UAVs may cooperatively process RANsignals to locate and identify either or both the UE(s) and thevehicle(s). In such cases, coordinated navigation among a cluster ofUAVs can enable locating and/or identifying the UE. In this case, RANmitigation can comprise cooperative navigation among the UAVs in whichnavigation is adapted according to the type of mitigation employed. TheUAVs can cooperate to intercept the RAN signal transmitted by thecontroller and/or vehicle. Simply listening to the signal and measuringits characteristics (such as may be done for channel estimation) issometimes called a passive attack. RAN mitigation can comprise activeattacks during which coordinated navigation among UAVs in a cluster isperformed to enable the attack. An active attack can comprise focusedRAN jamming of a UE and/or its vehicle, and can be implemented usingsimilar precoding techniques performed in cooperative MIMO. Other activeattacks include protocol manipulation of the RAN signals employed by thetarget UE(s) and/or vehicles, such as to cause the radios to perform ina manner that is within its normal operating parameters, but isinappropriate for given conditions or the operator's intent. In someaspects, an active attack may comprise hijacking the vehicleelectronically via its radio link, possibly exploiting vulnerabilitiesin its software, such as to land it in a safe location, direct it awayfrom a restricted airspace, or activate its homing routine. In aspectsof the disclosure, coordinated navigation is achieved via rule-basedautonomous navigation control aboard each UAV, wherein the rules arebased on relative spatial locations of other UAVs and may be adaptedbased on mission objectives. In some cases, the mission objectives areserved by adapting the rules, and thus navigation, based on RANmeasurements, which may establish RAN performance criteria. Theimplementation of such autonomous navigation can lead to swarmintelligence.

Swarm intelligence refers generally to the study of the collectivebehavior of multi-component systems that coordinate using decentralizedcontrols and self-organization. From an engineering point of view, swarmintelligence emphasizes the bottom-up design of autonomous distributedsystems that can provide adaptive, robust, and scalable behaviors.

As described above, a distributed MIMO communication system can comprisemultiple BTSs with multiple antenna elements, a CP, and a plurality ofremote receivers (e.g., UEs), wherein the multiple BTSs (and/or the CP)are configured to measure current channel status information (such as bysending training signals and receiving responses of the training signalsfrom the remote receivers, receiving training signals from the remotereceivers, performing channel estimation based on known characteristicsof data signals received from the remote receivers, and/or other blindadaptive processing of signals received from the remote receivers), thenupdating the current CSI and updating configurations of the MIMOpreprocessing. However, the multipath channel further comprises wirelesscommunication electronic systems on multiple platforms configured toactively scatter wirelessly communications signals originated from theBTSs toward multiple remote receivers.

In mobile ad-hoc networks, particularly in networks that employ UAVs asrelay nodes, the mobility of the network elements and (in some cases)the lack of central control can make cooperative-MIMO processingdifficult. In these networks, it can be advantageous to providecooperative-MIMO processing algorithms that are robust and adaptive andwork in a decentralized and self-organizing manner.

Aspects of the disclosure comprise decision processing and control ofthe active scattering platforms 111.1-111.4, 112.1-112.3, and113.1-113.3. Broadly considered, the field of cooperative decision andcontrol covers those interdisciplinary methods that can be used foroperating of semi-autonomous agents deployed to achieve a commonobjective. By exploiting the agents' capabilities, it is expected thatthe combined effort of the team can exceed the sum of its parts.Harnessing this potential benefit, however, is challenging due to thecomplexity that dealing with miscellaneous components in dynamic anduncertain environments brings about. In general, cooperative decisionand control algorithms find application in the supervision of largenetworked systems, such as sensor and actuator monitoring networks,supply-chain management systems, the power grid, and traffic systems. Aspecial and important use is the command and control of teams of UAVs,such as in surveillance and combat scenarios.

In some aspects of the disclosure, the active scattering platforms111.1-111.4, 112.1-112.3, and 113.1-113.3 are UAVs, and command andcontrol of the UAVs can provide for both flight management andcooperative-MIMO processing. In some aspects, cooperative flightmanagement (such as the concept of “free flight” in air traffic controlapplications) employs many of the same local communication andsituational awareness criteria that are useful for coordinatingcooperative antennas for MIMO processing. While conventional distributedair traffic control techniques provide only navigation management (e.g.,collision avoidance, maintaining safe aircraft spacing, optimal routing,etc.), aspects disclosed herein introduce MIMO processing criteria intothe navigation decision-making process. In some aspects, the MIMOprocessing criteria may be developed locally (e.g., by the UAVs),remotely (e.g., by the CP, BTSs, UEs), or some combination thereof.

Some aspects exploit the fact that cooperative flight management employsmany of the same local communication and situational awareness criteriathat are useful for distributed synchronization. Such aspects enable adecentralized, feedback-based synchronization architecture in which eachtransmitter adapts its frequency and phase independently based onfeedback from the receiver(s). In principle, this can allow the systemto scale to an indefinitely large number of cooperating transmitters.

Various types of routing algorithms can be used. Table-driven algorithmsare usually purely proactive: all nodes try to maintain routes to allother nodes at all times. This means that they need to keep track of alltopology changes, which can be difficult if there are many nodes and/orif the nodes are very mobile. Demand-driven algorithms are purelyreactive: nodes only gather routing information when a data session to anew destination starts, or when a route which is in use fails. Reactivealgorithms are in general more scalable, since they reduce routingoverhead, but they can suffer from oscillations in performance becausethey are usually not prepared for disruptive events. Routing algorithmsemployed in some aspects can be hybrid, using both proactive andreactive components.

In the UAV swarm, individual UAV nodes can have limited communicationrange, particularly for UAV-to-UAV communications. Data and controlpackets may need to be routed in a multi-hop modality. Datacommunications can be established between the nodes in the network inorder to support different activities. For example, UAV-to-AUVcommunications can comprise data transfers and control signaling forCooperative-MIMO processing. This can also include routing operationswithin the swarm and between clusters. Intermediate UAV sink nodes canindividually or cooperatively process data before sending the results toa global sink and/or can locally trigger the appropriate actions. Theglobal sink (which may be a UAV cluster head or the CP 130) performsfull data aggregation, as well as global processing operations, such asMIMO subspace precoding and/or decoding.

In some aspects, the design and implementation of routing schemes thatcan support efficient information exchanges and processing tasks in aCooperative-MIMO network can advantageously account for the followingconsiderations. First, the mechanisms adopted for route discovery andinformation routing should be energy efficient. Second, the networkshould have autonomic properties, meaning that the protocols in use mustbe self-organizing and robust to failures and losses. Finally, therouting protocol should be able to handle large and dense networks, andthe associated challenges resulting from the need to discover, maintain,and use potentially long multi-hop paths. Such criteria for efficientinformation exchanges can be implemented via adaptations to thenavigation rules employed by the UAVs.

FIG. 2 is a block diagram of a UAV in accordance with certain aspects ofthe disclosure. A flight controller 202 is communicatively coupled to asituational awareness system 201 and a cooperative RAN-signal processor203. A UAV fronthaul transceiver 204 can comprise a UAV fronthaul router232. The system 201 can comprise at least one or more onboard sensors211 and a GPS module 212. The processor 203 can comprise a UAV-UE CSIestimator 231, the UAV Fronthaul router 232, and optionally, a MIMOprocessor 233. The processor 203 is coupled to a UAV-BTS transceiver 206and a UAV-UE transceiver 207. Optionally, a fleet manager 205 may beprovided. The fleet manager 207 can comprise a UAV cluster manager 251,a synchronization manager 252, a MIMO processor 253, a scheduler 254, afronthaul network manager 255, and/or a mitigation coordinator 256.

The system 201 is configured to determine the UAV's relative spatiallocation with respect to at least one other UAV. The relative spatiallocation can include, for example, geographic location information(e.g., longitude, latitude, etc.) and/or directional path characteristicinformation (e.g., distance, altitude, directional heading, airspeed,and/or attitude). The relative spatial locations of nearby UAVs and/orUAVs assigned to a particular cluster can be determined. Onboard sensors211 can include active sensors, such as radar, lidar, acoustic rangingand detection system, as well as other active sensors, for determiningthe relative spatial location. Onboard sensors 211 can include passivesensors, such as cameras, any of the other types of imaging systems,radio receivers (including antenna-array receivers), and microphones, aswell as other passive sensors. Optionally, a GPS device, such as GPSmodule 212, may be provided. In some aspects, the system 201 may receivegeographic location information and/or directional path characteristicinformation from other UAVs via the UAV fronthaul transceiver 204. Forexample, GPS data and/or flight instrument data may be broadcast by UAVsover the UAV fronthaul network.

The flight controller 202 is an onboard system configured to facilitatecoordinated flight operations of two or more UAVs operating in closeproximity to each other. The flight controller 202 may use the relativespatial location information to adjust the UAV's directional path tomaintain a safe area around itself. The disclosed systems and methodsmay be implemented, for example, in an autonomous manner such as toprovide flight inputs to an aerial vehicle autopilot in order to makeadjustment in real time to the aerial vehicle flight path, e.g., theflight inputs may be input in a manner that does not alter existingwaypoints but rather alters the trajectory vector an aerial vehicle usesto obtain a waypoint.

The flight controller 202 performs tasks similar to those executed by apilot in a manned aircraft. Robust control techniques configured toadapt to changes in dynamics of the platform and the flight environmentare provided to enable autonomous flight. In some aspects, a flight planis input to the flight controller 202. By way of example, the flightplan may be determined by a ground control station or a UAV clusterhead. Other possibilities include distributed flight planning, such asflight-plan determination via a consensus of UAVs, ground stations, orsome combination thereof. The flight controller 202 employs a rule-basedflight coordination protocol (e.g., via computer processor/s or othertype/s of suitable processing component/s on each of the UAVs) toprovide effective de-confliction of flight paths on a real-time or nearreal-time basis.

In some aspects, the flight controller 202 can be configured to adaptthe rules of the flight coordination protocol based on variousenvironmental factors (which may be measured by corresponding sensors,such as meteorological instruments, as well as others) and/or missionobjectives. Furthermore, flight controller 202 can be configured tojointly process flight telemetry data with RAN performance data toprovide UAV mobility control that improves RAN communicationperformance. For example, flight controller 202 can adapt the UAV'sflight path to provide speed, heading, altitude, attitude, and/orposition with respect to nearby UAVs to enhance RAN connectivity withground terminals, such as UEs. Changes to the UAV's flight path can bemade to facilitate and/or enhance performance of cooperative RANprocessing between UAVs in a cluster.

In another aspect, the UE is targeted for mitigation. For example, theUE may comprise a radio controller for a radio-controlled vehicle thatis identified as a threat. The CSI estimator 231 or some other componentof the cooperative RAN signal processor 203 can generate RAN performancecriteria for one or more targets to be attacked (e.g., radio-controlledvehicles and/or their controller(s)). The RAN performance criteria canbe employed as RAN mitigation criteria by the flight controller 202 toadapt the UAV's flight in a manner that facilitates the UAV's ability toperform radio countermeasures against the target(s). In some aspects,RAN performance criteria can be configured to comprise additional RANmitigation criteria. In response to received RAN mitigation criteria,the flight controller 202 can adapt the UAV's flight to facilitatecooperative mitigation processing with other UAVs. For example, theflight controller 202 may be responsive to processor 203 in order toadapt flight navigation criteria according to the RAN performancecriteria, which can provide for enhancing RAN mitigation performance.The navigation criteria can be implemented by the flight controller 202as the rule base for autonomous navigation control. A selection oradaptation of the flight navigation criteria might be limited byprescribed navigation criteria boundaries, such as min/max altitude,min/max airspeed, min/max aircraft separation, airspace restrictions,and the like. The flight controller 202 might store the navigationcriteria and the navigation criteria boundaries in a memory. Then theflight controller 202 may employ the navigation criteria as an operatingrange of navigational parameters and/or target navigational parameter(s)(e.g., aircraft separation, altitude, attitude, aircraft separation, andthe like) to control the UAV's flight. In some aspects, flightcontroller 202 may communicate with other UAVs and adapt its navigationcriteria as a coordinated and/or responsive process to achieve a RANperformance objective.

In some aspects, RAN performance criteria that would be employed hereinfor the purpose of enhancing RAN performance can also be used tomitigate a RAN link (e.g., conduct active and/or passive attacks on theRAN transceivers using the link). In such aspects, RAN mitigationcriteria can comprise RAN performance criteria. By way of example,mitigation is a tactical response to a threat and can comprise directinga radio transmission signal at the target, such as to impair,manipulate, and/or hijack the target's RAN link while reducing oravoiding collateral RF damage (e.g., impairing the operation of othercommunication systems).

In one aspect, processor 203 comprises RAN channel estimator 231configured to measure and/or characterize at least one RAN channelbetween UAVs and at least one UE. For example, CSI, received signalstrength, bit error rate, transmission control messages, errordetection, error control messages, and the like can be used tocharacterize RAN link performance. Alternatively, RAN channel estimator231 receives CSI and/or other RAN link performance measurementstransmitted by UEs.

The UAV fronthaul transceiver 204 can comprise router 232. In oneaspect, the router 232 is part of the processor 203. However, the router232 may be separate, configured as part of transceiver 204, or otherwisedisposed within the UAV system. In one aspect, router 232 providesmulti-hop capabilities for relaying signals between the RAN and theBTSs. Signals from transceivers 206 or 207 may be routed via router 232to another UAV via transceiver 204. In some aspects, router 232 maycommunicate channel estimates from module 231, onboard sensor 211measurements, GPS 212 data, ground station control messages, fronthaulnetwork control messages, and/or fleet manager 205 control messages toother UAVs via the fronthaul network.

In some aspects, MIMO processor 233 employs the router 232 to distributeRAN signals to be jointly processed and (optionally) MIMO controlmessages to other UAVs. MIMO processor 233 can coordinate the UAVs in acluster to perform cooperative processing of RAN baseband signals.

In conventional MIMO, the RAN transceiver's 207 antenna system 217comprises an antenna array, which provides all the MIMO gain. But thelack of rich scattering in the UAS environment limits spatialmultiplexing gain. While some spatial multiplexing gain may beattainable, even in line-of-sight (LoS) channels, by carefully designingthe antenna separation with respect to carrier wavelength and linkdistance (e.g., F. Bohagen, et al., “Design of optimal high-rankline-of-sight MIMO channels,” IEEE Trans. Wireless Commun., vol. 6, no.4, pp. 1420-1425, April 2007), this requires large antenna separation,high carrier frequency, and short communication range. Another approachinvolves implementing multi-user MIMO by simultaneously servingsufficiently separated ground terminals with angular separationsexceeding the angular resolution of the antenna array installed on theUAV.

By enabling cooperation between UAVs, cooperative-MIMO processingsynthesizes extremely large antenna arrays with large antennaseparation. This dramatically increases spatial reuse and enhances RANperformance while enabling lower carrier frequencies and longercommunication range. The characteristics of the UAV-MIMO channelinfluence the performance of the UAV-MIMO data link. Therefore, inaspects of the disclosure, the positioning of UAVs in a Cooperative-MIMOUAV cluster can be adapted by each flight controller 202 to enhanceadvantageous characteristics of the UAV-MIMO channel to improve RANperformance.

The navigation criteria employed by the flight controller 202 can beadapted by processor 203 (such as via MIMO processor 233) within presetnavigation criteria boundaries. For example, RAN performancemeasurements (such as from estimator 231 and/or an estimator on at leastone other UAV) can be employed in a decision-making process to updatethe navigation criteria and instruct the flight controller to adapt theUAV's flight path to enhance RAN performance, such as performanceachievable via MIMO subspace processing.

In some aspects, centralized control of a UAV cluster can be implementedvia fleet manager 202. A UAV functioning as a UAV cluster head cancomprise the fleet manager 202, which can provide cluster management,including (but not limited to) one or more of assigning UAVs to operatein a cluster, assigning operations to the UAVs, scheduling resources tobe used by the UAVs, synchronizing the UAVs in a cluster, performingsynchronization with other clusters, providing navigation controlmessages to the UAVs, providing for fronthaul network management of thecluster's network, coordinating UAVs to perform mitigations, andcoordinating the UAVs to perform cooperative-MIMO processing of the RANsignals, which can include performing central processing of RAN signalsreceived by and/or transmitted by the UAVs in the cluster. MIMOprocessor 253 may communicate with each UAV's local MIMO processor 233.In one example, MIMO processor 253 directly or indirectly instructs eachUAV's flight controller 202 to adapt its flight according to enhance RANperformance. By way of example, the MIMO processor 253 may update thenavigation criteria employed by each UAV's flight controller 202.

In some aspects, distributed control of a UAV cluster is employed.Flight adaptations may be negotiated by flight controllers 202 on thedifferent UAVs in a cluster. In some aspects, feedback mechanisms may beemployed between UAV flight controllers 202 and these feedbackmechanisms may include rules to avoid oscillations and/or to quicklyconverge to an optimal cluster configuration. In certain aspects, UAVsin a cluster may adapt their flight paths in a manner that induces UAVsnot in the cluster to adapt their flight paths in a predictable manner.In one example, UAVs in a cluster employ an iterative process to induceother UAVs to move, such as to clear a space for a predetermined clusterconfiguration to occupy. In certain aspects, UAVs may transmit theirintents to other (e.g., nearby) UAVs so those UAVs can more efficientlyadapt their flight paths. UAVs may be provided with different levels ofpriority, and those with higher priority may move UAVs with lowerpriority out of the way. Priority levels may be determined by acluster's mission, a UAVs function in the cluster, each UAV's RANperformance measurement, maneuverability, processing capability, batterylife, and/or other criteria.

The fleet manager 205 can comprise a system aboard the UAV and/or it canbe implemented via one or more remote systems communicatively coupled tothe UAV. The fleet manager 205 can be implemented as a central ordistributed system. Any of the components 251-256 can be implemented viacentralized or distributed means. In one aspect, the fleet manager 205resides on the cluster head of a UAV cluster. In another aspect, fleetmanager 205 resides in one or more ground stations. In another aspect,fleet manager 205 resides on each UAV but may comprise differentfunctionality depending on the UAV on which it resides.

In some aspects, fleet manager 205 is implemented on a combination ofdevices, including UAVs and ground stations. For example, MIMO processor253 may be implemented on each UAV in a cluster (such as for local MIMOprocessing), and MIMO processor 253 may be implemented in a data centercoupled to the BTSs (such as for global MIMO processing). Clustermanager 251 may reside on the cluster head and may create and adapt UAVclusters based on various factors, such as (but not limited to)proximity of UAVs, UAV capabilities, RAN demand topologies, UAV networktopologies, geographical locations of UAVs, UAV battery life, channelmeasurements (fronthaul and/or RAN), etc. In some aspects, clustermanagement may be performed or supplemented by ground control stations.

Sync manager 252 may reside on a cluster head and/or one or more UAVs ina cluster configured to help synchronize signal processing andcommunications. In some aspects, synchronization may compriseground-based synchronization and/or GPS-based synchronization. Syncmanager 252 can synchronize UAV clocks and provide for synchronous RANtransmissions and provide a timing reference for UAV fronthaulcommunications. Sync manager 252 may be configured for variouscalibration functions, such as to compensate for phase drift, frequencyoffsets, and timing offsets. In one aspect, sync manager 252 helps tocalibrate local oscillators onboard the UAVs.

Scheduler 254 can comprise different types of scheduling functions. Forexample, RAN resource scheduling may be performed by ground stations,such as to assign RAN resources to each UE. In some aspects, scheduler254 residing on a cluster head schedules which UAVs serve each UE. Insome aspects, an SDR resides in the ground stations, such as in a datacenter coupled to the BTSs. The SDR spawns an SDR instance configured toserve each UE. In some aspects, the SDR instance selects the BTSantennas to serve its UE. The SDR instance may select which UAVs and/orUAV antennas to serve the UE, or the SDR may allow the cluster head toselect the UAVs and/or UAV antennas. In some aspects, at least somedetails of how the RAN link is implemented are hidden from the SDR. Insome aspects, scheduler 254 residing on one or more UAVs selects whichUAVs in a cluster are active RAN transceivers. Scheduler 254 candetermine which UAVs process RAN signals, such as calculating MIMOweights. Scheduler 254 may assign different signal processing tasks tothe UAVs based on any of various criteria, including battery life,processing capability, storage capacity, among others.

Fronthaul network manager 255 can reside on one or more UAVs in acluster and can be configured to provide for routing topologies, storagetopologies, and/or processing topologies in the cluster. Manager 255 canprovide routing tables for router 232. Manager 255 may be responsive toUAV fronthaul link quality to instruct the flight controller 202 onboardany of the UAVs to adapt flight path(s) to enhance or ensure UAVfronthaul communications, which can include inter-cluster and/orintra-cluster communications.

Mitigation coordinator 256 can reside on one or more UAVs in a clusterand can be configured to provide for mitigating a threat, such as bycoordinating an attack on a RAN communication link employed by a targetUE. For example, mitigation coordinator 256 can direct one or more otherUAVs (such as UAVs in a cluster) to manipulate a RAN transmissionprotocol used by a targeted controller device to remotely control avehicle, and/or a RAN transmission protocol used by the controllerdevice to receive telemetry, video feeds, and the like from the vehicle.Mitigation of the RAN link can comprise employing MIMO processor 253,for example, to disrupt the RAN link, preferably in a manner that avoidsdisrupting other communication links. Mitigation can compriseeavesdropping on the target's RAN link, employing countermeasuresdesigned to hijack control of the RAN link, and/or exploiting softwarevulnerabilities to acquire control of the vehicle. Mitigationcoordinator 256 may operate, for example, with CSI estimator 231,radars, and/or other systems to provide geolocation and tracking of thecontroller device and/or vehicle(s) and may communicate this informationto other systems that may provide for neutralizing the threat. In oneaspect, mitigation coordinator 256 is responsive to the target'scommunications and/or movement for determining the effectiveness ofprescribed mitigations. Coordinator 256 may operate with the scheduler254 to schedule adaptations to mitigations performed by the UAVs, suchas based on their determined effectiveness and/or featurecharacterization of the RAN protocol employed by the target.

FIG. 3A is a flow diagram that illustrates a method according to anaspect of the disclosure. The use of maneuverable UAVs for RAN terminalsoffers new opportunities for performance enhancement through the dynamicadjustment of UAV clusters to best suit the communication environment.Cooperative communications can be jointly designed with UAV mobilitycontrol to further improve the communication performance. A UAV isprovided with spatial location information of nearby UAVs 301. The UAVperforms cooperative RAN processing with at least one other UAV 302 in aUAV cluster. Inter-UAV communications are provided 303 to enablecooperative RAN processing. Autonomous UAV navigation (e.g., flightcontrol) is based on the spatial location information and navigationcriteria, which is determined, in part, by RAN performance criteria 304.

FIG. 4 illustrates a procedure that employs steps 301 and 304. In someaspects, the procedure is implemented by a computer processor accordingto instructions of a software program configured to operate on inputphysical measurements described herein. A UAV takes sensor readings 401at regular intervals to determine a relative spatial location withrespect to at least one nearby UAV. In some aspects, the sensor readingscomprise GPS coordinates. In one aspect, GPS coordinates derived by GPSreceivers on other UAVs are conveyed via a fronthaul network to providethe sensor readings in step 401. The sensor readings are compared 402with navigation criteria 410. If the sensor data indicates a navigationparameter (e.g., separation between UAVs, relative speed, variation inheading, variation in altitude, etc.) outside a predetermined range(e.g., navigation criteria), the UAV's flight path is adapted 403 by itsflight controller 202, and then sensor readings are monitored 401 in afollowing time interval. Otherwise, the flight controller 202 orsituational awareness system 201 continues to monitor the sensorreadings 401.

Systems and methods may be employed to communicate the locationinformation between two or more UAVs or other entities, and may be usedto facilitate coordinated operations of two or more such entities. Inone example, a UAV is made aware of one or more location (e.g.,longitude, latitude, etc.) and/or flight characteristics (e.g.,altitude, directional heading, airspeed, attitude, etc.) of one or moreother adjacent aerial vehicles, and the UAV may use that locationinformation to adjust its flight path to maintain a safe sphere of emptyairspace around itself. The disclosed systems and methods may beimplemented without need for a ground system and/or human controller tomicro-manage the UAVs. Each of the aerial vehicles may be made aware ofother aerial vehicles in its immediate airspace and, when appropriate,take evasive action to avoid collision. A rule-based flight coordinationprotocol may be implemented (e.g., by computer processor/s or othertype/s of suitable processing component/s on each of the UAVs) toprovide effective de-confliction of flight paths on a real-time or nearreal-time basis. Such a rule-based flight coordination protocol may beconsistent and simple.

RAN performance measurements may be monitored concurrently 411 withsensor monitoring 401. RAN performance measurements may be produced bythe estimator 231. The RAN performance is evaluated to determine if theflight path should be adapted to improve RAN performance 412. This maybe performed by the flight controller 202 or processor 203. In oneaspect, the navigation criteria 410 can be updated 413 withinpredetermined navigation criteria boundaries 420. Such boundaries can bedesigned to ensure safe distances between UAVs, and can be selectedbased on UAV maneuvering capabilities, number of UAVs in a cluster,cluster density, weather conditions, as well as other factors. Theboundaries may be imposed by various regulations, including (but notlimited to) restricted airspaces, altitude regulations, and airspeedrestrictions. The boundaries and/or navigation criteria may be selectedbased on additional criteria, such as UAV-to-UAV fronthaul communicationrange, fronthaul network topology, optimizing UAV power efficiency, aswell as other factors and/or objectives. The boundaries might bedetermined based on operating objectives of the UAV, such as deliveringgoods to a specific location, performing surveillance, or performingother mission that might require a preset flight plan that permits alimited amount of variations thereto. Updating the navigation criteria410 may result in step 402 triggering the flight controller 202 to adaptthe flight path 403, since the navigation criteria 410 can beimplemented as the rule base for autonomous navigation control.

In one aspect, the UAV flight system comprises motors and propellerswith control provided by an onboard flight controller/stabilizationboard (e.g., flight controller 202) selectively throttling the motors inresponse to control signals to adapt the flight path 403. One type ofUAV is the multirotor or multicopter. This UAV is a rotorcraft with morethan two rotors, and multicopters often use fixed-pitch blades withcontrol of vehicle motion being provided by varying the speed of eachrotor to change the thrust and torque. Flight path adaptation 403 caninclude adapting the UAV's attitude. Control systems for a vehicle'sorientation (attitude) include actuators, which exert forces in variousdirections and generate rotational forces or moments about theaerodynamic center of the aircraft, and thus rotate the aircraft inpitch, roll, or yaw.

In one aspect, step 412 comprises quantifying MIMO channel quality frommeasurements received from step 411. The condition number of the MIMOchannel matrix H is defined as the ratio of the maximum and minimumcharacteristic values of channel matrix H. In the MIMO system, as thecondition number approaches one, the quality of the parallel spatialchannels increases. When the ratio becomes larger, the MIMO channelquality diminishes. Based on the calculation method of UAV-MIMOnormalized channel correlation matrix, processor 203 can analyze thecharacteristics of the UAV-MIMO channel.

The flight control problem in step 412 for UAV deployment can involvefinding the optimal UAV separations, hovering altitude, and geographicallocation to enhance coverage in addition to RAN performance. But thereare trade-offs. When the separation between antenna elements increases,the condition number of the channel matrix decreases, which improves theMIMO channel quality. However, the maximum separation betweenneighboring UAVs may be restricted based on navigation criteriaboundaries and/or other factors, such as UAV fronthaul considerations,(e.g., frequency reuse objectives and maximum effective range). Atlarger separations, there can be diminishing benefits. Thus, updatingthe navigation criteria 413 can employ a cost-benefit trade-off betweenmultiple factors.

Increasing the distance between UEs also decreases the condition number,which can improve the RAN MIMO channel. Thus, UAV cluster configurationcan include scheduling, such as scheduling which UEs share RAN resourceblocks in order to enhance frequency reuse.

Besides the antenna separation, the azimuth angle and pitching anglealso influence the average channel capacity. For example, a smallerpitching angle can provide larger average channel capacity. Also, theUAV antenna might be oriented horizontally to provide a larger channelcapacity.

For a particular environment, there might exist an optimal UAV altitudefor coverage maximization, which is due to the following tradeoff: Whileincreasing UAV altitude will lead to higher free space path loss, italso increases the possibility of having LoS links with the groundterminals. However, higher UAV altitudes lead to higher conditionnumbers, which inhibits MIMO performance.

The average MIMO channel capacity is smaller when UAVs are more distantfrom the UEs. This is because the greater geographical distance reducesthe spatial resolution, and is also related to reduced multipath.

The characteristics of the UAV-UE channel influence the performance ofUAV-based cooperative MIMO to a great extent; therefore, the positioningof UAVs in a Cooperative-MIMO cluster can be adapted to enhanceadvantageous characteristics of the cooperative-MIMO channel to improveperformance. In some aspects of the disclosure, UAV flight paths aredetermined not only by interactions with each other (such as to providecourse adjustments, aircraft spacing, etc.), but also by measured and/orcalculated parameters in a multi-dimensional search space correspondingto radio communications with a set of UEs. In a population-based searchalgorithm, optimization can comprise minimizing a cost function ormaximizing a benefit. In some aspects of the disclosure, optimizationcomprises determining UAV locations (including flight patterns) thatreduce cost functions and/or increase benefits, but within constraintsgoverned by avionic systems (e.g., max/min speed, altitude,maneuverability, etc.), air traffic control (e.g., situationalawareness, minimum required aircraft spacing, optimal spacing, max/minaltitude, predetermined flight paths, intended destination, etc.), andoptionally, environmental factors (e.g., weather, terrain, buildings,etc.).

Particle swarm optimization (PSO) is a population-based search procedurein which individuals (particles) change their position in amulti-dimensional search space with time. In PSO, the multidimensionalsearch space comprises a set of potential solutions. During “flight” inthe search space, each particle adjusts its position on the basis of itsown experience and on the basis of the neighboring particles'experience, making use of the best position encountered by itself andits neighbors. Thus, a PSO system combines local search methods withglobal search methods. Therefore each particle has a tendency to move ina direction of sequentially improving solutions

In accordance with some aspects of the disclosure, each PSO particlecomprises a UAV. In some aspects, a particle may comprise a group ofUAVs, and in some aspects, the selection of UAVs in such a group canchange. The UAVs physically move (i.e., fly) in actual three-dimensionalspace, and their flight paths are determined not only by interactionswith each other (such as to provide course adjustments, aircraftspacing, etc.), but also by measured and/or calculated parameters in amulti-dimensional search space corresponding to radio communicationswith a set of UEs.

In a population-based search algorithm, optimization can compriseminimizing a cost function or maximizing a benefit. In some aspects ofthe disclosure, optimization comprises determining UAV locations(including flight patterns) that reduce cost functions and/or increasebenefits, but within constraints governed by avionic systems (e.g.,max/min speed, altitude, maneuverability, etc.), air traffic control(e.g., situational awareness, minimum required aircraft spacing, optimalspacing, max/min altitude, predetermined flight paths, intendeddestination, etc.), and optionally, environmental factors (e.g.,weather, terrain, buildings, etc.).

In Cooperative-MIMO communications, the channels conditions can vary intime rapidly and the communication system may need to change itsselection of the antennas frequently in order to maintain highperformance. Computational efficiency of the antenna selection algorithmis important for adapting the antennas selection quickly to changingchannel conditions. Due to the high computational complexity of theoptimal selection, a suboptimal solution with lower complexity can beadvantageous.

FIG. 3B is a flow diagram that illustrates a method according to someaspects of the disclosure. In step 304, autonomous UAV navigation canemploy a rule base configured in accordance with energy-efficientoperation parameters. Step 305 provides for processing RAN signals inthe UAV-UE link, such as to provide RAN performance enhancement.

Energy-efficient operation aims to reduce unnecessary energy consumptionby the UAVs. As the primary energy usage of UAVs supports aircraftpropulsion and wireless communications, energy-efficient operationschemes can be classified into two categories. The first one isenergy-efficient mobility, for which the movement of the UAVs should becarefully controlled by taking into account the energy consumptionassociated with every maneuver. Energy-efficient mobility schemes can bedesigned with path planning optimization by using appropriate energyconsumption models as a function of UAV speed, acceleration, altitude,etc. Such models can provide a basis for adapting either or both thenavigation criteria and the navigation criteria boundaries.

The other category of energy-efficient operation is energy-efficientcommunication, for which the communication requirement is satisfied withreduced energy expenditure on communication-related functions, such ascomputer processors, amplifiers, etc. To this end, one common approachis to optimize the communication strategies to maximize energyefficiency in bits/Joule (e.g., the number of successfully communicateddata bits per unit energy consumption). Such an approach can be used toadapt the navigation criteria.

FIG. 3C is a flow diagram that illustrates a method according to someaspects of the disclosure in which a step 306 is provided to mitigate atleast one UE's RAN link. In this case, step 302 can comprise identifyinga UE transmission, measuring the UE channel characteristics, and/orprocessing the received UE transmission to identify the signal type,radio transmitter type, and/or remote-controlled vehicle type. The UEchannel characteristics, which may comprise channel characteristicsbetween the UAV(s) and the vehicle the UE is controlling, can be used toadapt navigation criteria 304 and/or provide for mitigation 305, such asto track the vehicle and/or efficiently direct transmittedcountermeasures. Adaptation of the navigation criteria 304 can result inflight adaptations designed to enhance RAN mitigation performance. Byway of example, but without limitation, the navigation criteria can beadapted 304 to enhance cooperative RAN mitigation performance of a group(e.g., cluster) of UAVs assigned to cooperatively mitigate a target. Insome aspects, the signal type, radio transmitter type, and/orremote-controlled vehicle type can be used to formulate a mitigation 305plan for the UAV and/or the group.

FIG. 5 illustrates relationships between navigational directives and RANdirectives associated with managing a swarm of UAVs in accordance withaspects of the disclosure. At least some of the RAN directives can beconfigured to adapt at least some of the navigational directives. Thenavigational directives include individual directives 501-506, and theRAN directives comprise individual directives 511-516.

The directives 501-506 may comprise simple rules, such as move in thesame direction as your neighbors 501, avoid collisions 502, remain closeto your neighbors 503, abide by altitude and airspace restrictions 504,maintain a particular flight formation relative to your neighbors 505,and at least one of the members of the swarm has information about thegroup heading 506, such as an intended destination or a flight plan withspecific way points.

The directives 501-506 may comprise navigation criteria based on sensormeasurements, such as a threshold value or a range of permissiblevalues. For example, avoid collisions 502 might establish minimumaircraft separation. In some cases, such as in the case of directive502, the navigation criteria might comprise a boundary which cannot bechanged by a RAN directive. However, other factors (such as weatherconditions) might affect the boundary. In other cases, a navigationdirective, such as directive 503 (remain close to your neighbor), canhave its threshold or range adapted by a RAN directive, possibly withinpredetermined boundaries.

The directives 511-516 may also comprise simple rules based on localdata collection, local processing, and/or remote processing. In someaspects, the RAN directives 511-516 influence the UAV navigation withinthe bounds of the rules (e.g., 501-506). In some aspects, RAN directivesmay change one or more of the navigation rules 501-506.

Adaptation to geographical UE densities 511 can comprise selecting theheading of one or more UAVs, such as to form a cluster of UAVs or todirect at least one existing cluster in a swarm to service UEs in aparticular geographical location. The heading selection can be performedin response to UAVs indirectly detecting UE densities, such as via RANchannel activity. Similarly, BTSs and/or CPs can instruct the UAVs tochange their headings in order to adapt to detected UE densities. In oneaspect, the RAN adapts the selection of antennas employed on either sideof a MIMO channel, such as to increase the MIMO channel rank, improvediversity, balance power loads across multiple devices, and/ordistribute processing loads across devices. By way of example, when thenumber of UEs in a particular geographical area exceeds the number ofserver-side antennas configured to serve the UEs (e.g., the total ofavailable base transceiver antennas and UAV antennas provisioned toserve a set of UEs), the UEs, the BTSs, the CP, and/or the UAVs may callfor additional UAVs to support cooperative-MIMO processing in the RAN.Furthermore, it should be appreciated that UEs can be provisioned tosupport server-side operations, such as increasing the rank of the MIMOchannel matrix, relaying signals, and providing computational resources.

In some aspects, conditions for navigational directives can be adaptedrelative to one or more RAN directives. For example, rules 501 and 503may be implemented via an algorithm that weighs certain advantages, suchas power savings from flying in a group formation with other UAVs (whichreduces drag), against disadvantages of not following the most directcourse to a predetermined location. The algorithm might choose a paththat allows the UAV(s) to fly with a particular group until a certainpoint at which a new course is selected and the UAV(s) break away fromthe group. In some aspects, different groups in a swarm negotiate acourse for the swarm based on the needs of the groups or individualUAVs. A least squares type of method or other regression analysis methodcould be used to plot a course for the swarm that optimizes at least oneparameter (such as a function of power savings and expediency) thatcharacterizes how well-served the UAVs are.

Some conditions, such as the criteria of rule 501, might be bentslightly under the influence of RAN directives. For example, a UAV canmove generally in the same direction as its neighbors while employing asmall degree of divergent movement, such as to change its locationwithin the swarm or to vary its course, such as in accordance withperturbation techniques disclosed below.

In some aspects, navigation directives and RAN directives can be thesame. For example, the rule “remain close to your neighbors” 503 can beharmonious with the rule “ensure local area communications” 513, sinceone aspect of maintaining communications between UAVs includes stayingwithin a specified range. In another aspect, a swarm might comprisemultiple clusters, and UAVs within a cluster might need to be spatiallyseparated by a sufficient distance such as to achieve sufficient channelrank or reduce the condition number 512, synthesize a sufficiently largeaperture (not shown), or any other number of reasons. However, thenavigation rule 503 can still be maintained with respect to UAVs thatbelong to other groups. Thus, as demonstrated here, RAN directives canadapt one or more of the navigation directives. In some case,conflicting RAN directives can be resolved by determining compromise RANcriteria, such as may be based on a cost-benefit optimization. Thus, oneRAN directive might affect conditions specified by another RANdirective.

While the navigation directives tend to correspond to decentralizedoperations, some of these directives can be provided by a centralizedcontrol operation, such as a flight plan 505 (in some aspects, theflight plan 505 comprises only a destination) and group heading 506. Forexample, the flight plan 505 might be determined by the CP 130 (e.g.,the CP 130 being responsive to UE requests for service, selecting UAVs,and directing them to serve the UEs). The group heading 506 might bedetermined by a central controller within a UAV cluster (e.g., a clusterhead).

In accordance with certain aspects, there is a hierarchy with respect tocontrol directives. In one aspect, control directives from a remotecentralized authority can be overridden by control directives from alocal authority. For example, with respect to operations performed by aparticular UAV, a CP might be overridden by a UAV cluster head or by theparticular UAV. Some directives, such as avoid collisions 502 takesprecedence over other directives, such as flight plan 505. Suchoverriding control directives may be temporary, and adherence to controldirectives issued by the central authority may resume thereafter. Inanother aspect, a centralized authority (e.g., the CP 130) can overridecertain control directives from a local authority, such as a UAV clusterhead. In some aspects, a central authority (e.g., the CP 130 and/or aUAV cluster head) can override a UAV's individual directives.

In one aspect, each of a set of UAVs performs local MIMO processingoperations, and then the results of the local MIMO processing arecommunicated to a central authority, which performs global MIMOprocessing with the results. By way of example, a cluster of UAVs mayperform local cooperative-MIMO processing, such as to determine subspaceprocessing weights from local CSI. The local MIMO processing results(e.g., local subspace weights and, optionally, the CSI) can becommunicated to a central authority which receives local MIMO processingresults from other clusters. Upon collecting local MIMO processingresults from multiple clusters, the central authority can perform globalMIMO processing with the results.

In some aspects of the disclosure, the local results may constitute afirst estimate for the MIMO processing weights, and global processingmight provide improved estimates, which can then be used to performspatial multiplexing and/or demultiplexing. In some aspects, global MIMOprocessing can constitute evaluating multiple local MIMO processingresults followed by the central authority instructing the clusters toprovide a particular perturbation or predetermined adaptation to theirweights.

It should be appreciated that aforementioned local and global processingmay be performed on different scales. By way of example, localprocessing may comprise a wireless network node's processing of its ownantenna array signals, in which case global processing could comprisecooperative processing of multiple nodes' signals, such as by a clusterhead. Local processing may comprise a cluster of such nodes performingcooperative processing, in which case global processing could compriseprocessing signals from multiple clusters, such as by a super-clusterhead or by the CP 130. Local processing could comprise processingsignals received from multiple clusters, such as in the CP 130, in whichcase global processing could comprise processing signals betweenmultiple CPs. Thus, global processing can help mitigate interferencebetween nodes, between clusters, and/or between CPs, depending on thescale. It should also be appreciated that a processing hierarchy mayprovide for one or more levels of processing between local and globalprocessing.

In one aspect of the disclosure, a gradient technique can be employed toadapt a cluster's heading in order to better serve a group of UEs. Inone example, each UAV in a cluster measures a transmission from a UE setcomprising one or more UEs. A measurement may comprise signal power,SNR, channel measurements, or the like. The measurements are thenprocessed with the flight formation of the cluster. If measurements fromUAVs at one side of the flight formation are superior to measurementsfrom UAVs on at least one other side, then an algorithm can be employed(e.g., by the cluster head) to map a new heading vector for the clusterin the direction of the measurement gradient.

Another aspect of the disclosure employs gradient type of routing in adistributed network as a means for delivering network services and maybe used for configuring the network to adapt to changing network loads.For example, nodes in a network (e.g., UAVs) can share qualitymeasurements with each other. Since the local area links employed byUAVs to communicate with each other can be short range, ultra-highbandwidth links (e.g., UWB, optical wireless, etc.), bandwidth overheadis generally not a problem. These quality measurements may comprisemeasured power of a UE's transmission, SNR, CSI, etc. These qualitymeasurements may comprise calculations based on the aforementionedmeasurements, and may comprise eigenvalues, channel correlation, varioussubspace channel quality indicators, etc. Data intended for a particularUE or set of UEs may be routed through the network wherein the routingpath is determined by successively improving quality measurements. Thus,the routing path may follow a sequence of nodes having successivelyincreasing quality measurements. The data follows the qualitymeasurement gradient until a predetermined quality measurement conditionis met, such as a threshold quality measurement value, a maximum (localor absolute) value, etc., at which point it has found its gradientrouting destination relay node. The data may be transmitted to the UE(s)from that node via RAN transmission, or it may be distributed to atleast one other node (such as nodes in a cooperative-MIMO cluster)before being transmitted in the RAN.

In one aspect of gradient routing, routing information may beestablished on demand, but instead of a cost for delivering a message,messages are passed among nodes with successively better RANconnectivity to the destination UE. For example, in a first case, eachnode has a quality measurement table that stores quality measurements ofits neighbors. If upon receiving the message, the node determines thatone of its neighbors has a better quality measurement, it routes themessage to its neighbor. In a second case, each node only knows its ownquality measurements (unless perhaps those measurements are pertinent tocooperative-MIMO processing in a cluster to which the node belongs). Anode listens to a broadcast from its neighbor comprising a message andindicating the neighbor's quality measurement corresponding to themessage's destination UE. Sometimes multiple broadcasts may occursimultaneously with the same message, in which case the node paysattention only to the broadcast with the highest quality measurement. Ifthe node has a better quality measurement than indicated in the receivedbroadcast, then it rebroadcasts the message along with its qualitymeasurement, and then it listens for any broadcast that indicates themessage was rebroadcast by a subsequent node with a better qualitymeasurement. While flooding results in a high data bandwidth overhead,the use of extremely high bandwidth local-area links (such as optical,UWB, etc.) minimizes this. However, in some aspects, techniques may beemployed for removing duplicate broadcast messages. In a third case,each node only knows its own quality measurements, as in the secondcase. However, when a node has a message to send, it polls its neighborsfor their corresponding quality measurements specific to the destinationUE. Then the node routes the message to the neighbor with the bestquality measurement.

In one aspect of the disclosure, a method of gradient routing comprisesdata to be transmitted to a particular UE being routed over a fronthaulnetwork from a CP to at least one base transceiver terminal. The atleast one base transceiver terminal transmits the data to at least oneof a set of relay nodes, such as UAVs. The data is routed through therelay nodes using a gradient routing algorithm based on qualitymeasurements corresponding to its destination UE. Once the data isdelivered to its destination relay node, it is either transmitted to itsdestination UE, or it may be distributed to other relay nodes configuredto cooperatively transmit signals to the destination UE.

In accordance with one aspect of the disclosure, the apparatus depictedin FIG. 6 can be configured to perform the following method. Theapparatus as a whole, or any of its components, can be implemented in acentralized or distributed manner. In aspects of distributed operation,one or more blocks may comprise components residing on multiple UAVs. Inaspects of centralized operation, the apparatus resides in a centralcoordinator (such as a CP or a UAV cluster head).

Fleet Manager 605 uses a Cluster Manager 651 to coordinate navigation ofindividual UAVs in the cluster via flight rules, which can include acombination of global flight control directives (e.g., flight plans,flight boundaries, obstacle locations, etc.) and local flight controldirectives (e.g., aircraft separation, collision avoidance, otherautonomous flight control rules, etc.). Some directives can be providedwith higher priority than other directives such that the UAV responds tosuch directives according to a hierarchy of priority. For example, a UAVmight enact a global flight control directive within boundariesestablished by its local flight control directives.

Inputs to the Cluster Manager 651 are received from SituationalAwareness Manager 601, and can comprise UAV spatial location data forUAVs in a cluster. This data may include UAV flight telemetry data,remote sensing data, other UAV position data, and/or combinationsthereof. Situational Awareness Manager 601 can comprise a FlightTelemetry Data Manager 611, a Sensor Network Manager 612, and,optionally, a Threat Detection System 613. Cluster manager 651 mayreceive inputs from Cooperative MIMO Processor 653 to select globaland/or local flight directives configured with respect to cooperativeMIMO performance criteria, and may select the flight directives withinpredetermined boundaries of navigation criteria.

Threat Detection System 613 can be configured to target UEs and/orremote-controlled vehicles for mitigation and send correspondinglocation data to the Fleet Manager 605. Fleet Manager 605 via Scheduler652 can schedule particular UAVs to perform countermeasures against thetarget and/or select flight control directives to transmit to particularUAVs, such as to coordinate an attack. In such aspects, Processor 653can comprise a mitigation system (not shown) configured to exploit atarget's radio link to implement a passive and/or active attack.

For distributed processing, Cooperative MIMO Processor 653 communicateswith a Fronthaul Network Manager 655, which communicates with theCluster Manager 651. Fronthaul Network Manager 655 communicates networktopology criteria to the Cluster Manager 651 to adapt Global and/orLocal flight control directives to adapt a fronthaul network topology.

Cluster Manager 651 via the Scheduler 652 schedules UAVs to operate in acluster. Scheduler 652 can assign UAVs to a cluster. By way of example,Scheduler 652 operating in cooperation with the Processor 653 and/orManager 655 can assign duties to individual UAVs, including signalprocessing, data storage, and/or routing functions.

Responsive to operating criteria input by the Cooperative MIMO Processor653, the Fronthaul Network Manager 655 formulates the network topologysuch that a network routing topology operates with links within definedtolerances, such as below a latency threshold, above a data bandwidththreshold, above a QoS threshold, above a reliability threshold, above aBER threshold, above an SNR threshold, and the like. Fronthaul NetworkManager 655 may request that Cluster Manager 651 adapt UAV spatiallocations to enable the network to operate within the definedtolerances.

Cooperative MIMO Processor 653 is configured to perform distributedbeamforming. Use of the term “distributed” has two distinct meanings inthe sense of distributed beamforming. The first meaning indicates thatthe antennas of the array are distributed over the receiving plane,possibly in some randomly structured fashion. This is a departure fromtraditional beamforming, which relies on a strict, uniform placement ofthe antenna elements to reduce the complexity of the analysis throughthe removal of dependence on the individual locations of nodes withinthe arrays. When the node locations are no longer structured, thelocation of each element must be considered on its own, rather thansimply considering the location of the array as a whole. In thisscenario, the elements are still controlled by some central source; thusthe locations, phase offsets, and transmit capabilities of each nodecould be known quantities which are used to produce weight calculations.The second meaning builds on the first, implying that the elements arenot only distributed in terms of location, but are also independentprocessing units, such as UAVs and UEs. This second scenario typicallylimits the quantity and quality of information available to abeamformer.

In one aspect, methods for determining complex weights are distributedin the sense that they can be performed by each node individuallywithout sharing significant amounts of information. In another aspect,the nodes can share the total amount of information about themselves,such as through some pre-communication phase. In this case, nearly idealweights might be calculated based on the global information anddisseminated through the fronthaul network by a single cluster head. Acombination of these aspects can be performed. For example, opticalcommunication enables vast amounts of data bandwidth, particularly forshort-range communications, so lots of CSI (as well as MIMO processinginformation) can be shared locally. This can facilitate distributedprocessing, such as cloud computing. This provides a central coordinator(e.g., a cluster head) with enough information to perform global MIMOprocessing (or at least one level above local MIMO processing) whileenabling the central coordinator to exploit distributed computingresources, the availability of which can increase with thecooperative-MIMO array size.

A method for determining complex MIMO weights according to one aspectcomprises the following steps. First, a constraint is chosen, such asthe SNR at the receive nodes. However any quantifiable quantity can bechosen, such as the capacity of the total link, the total powerconsumption, the per-node power consumption, etc. Second, an analyticalderivation for the optimized value of interest is created based on therelay network model, and a method for iteratively reaching that optimumis presented. Finally, the problem is broken up such that thetransmitter array and/or the receiver array can calculate a singlecoverage parameter that leads the individual nodes to find their ownoptimum weights, distributing the calculation over the network.

When a target is moving, or ideal weight calculations are not possibledue to lack of information, an adaptive method may be used to home in onideal weights, such as by iteratively changing the phases based on thearray performance. This leads to distributive smart antennas that arecapable of compensating for movement within the array, target, source,channel, and interferers, and adapting to other changes in the channel.In order to arrive at the ideal weights (or at least a local maxima)without full CSI, attempts to iteratively find the optimum weighting ateach relay using one bit feedback from the destination node can be used.For example, using plus/minus perturbation, the next weight is perturbedtwice during transmission, and the feedback bit specifies which of thetwo is best.

In another aspect, a cluster of UAVs providing RAN service to a set ofUEs is configured to adapt navigation parameters to improve the RANservice. A perturbation to course, cluster flight formation, and/oraltitude can be used to adapt the cluster to seek improved arrayperformance. For example, a constraint is chosen, such as SNR,eigenvalues corresponding to MIMO eigenvectors, or another measurableparameter. Based on the constraint, the UAVs can measure the constraintdirectly from signals received from the set of UEs, and/or the set ofUEs can measure the constraint and return feedback information to theUAVs. Then the perturbation to the cluster's heading and/or other flightcharacteristics is performed. Measurements of the constraint(s) are madeby the UAVS and/or the UEs and are processed (possibly by at least onecentral authority) to distinguish the effects due to the perturbationfrom effects due to other changes in the channel. There are manytechniques to distinguish the perturbation effects from other effects,and the invention is not limited to particular techniques. In oneaspect, effects characterized by variances having a zero mean can beaveraged out with a sufficient number of samples.

In accordance with one aspect, a singular value decompositions (SVD) canbe used to obtain beamforming weights using global CSI. The SVD methodallows the transmitter to precode data x sent over a MIMO channel h withthe left decomposition of the channel v and the receiver to decode withthe right decomposition u, giving the received vector:y=u ^(H) Hvx+u ^(H) n

An iterative method can be used for calculating the right SVD vector,relying on blind adaptive methods for calculating of the left. Thismethod works by treating the multiple paths as parallel SISO links withgain specified by the diagonal elements of the SVD, allowing nodes tocalculate their ideal weights based on their local element. The amountof information sent over the feedback channel can be reduced by using apredictor to estimate the value of the current singular vectors at eachtransmitting node rather than feeding back the vector in each iteration.After each iteration of the transmission, the values of the singularvector are transmitted back to the relay if the difference between theestimated values and the calculated values exceed a set threshold,allowing for a balance between performance and the overhead in thecontrol channel.

Precoding algorithms for SDMA systems include linear and nonlinearprecoding types. The capacity achieving algorithms are nonlinear, butlinear precoding usually achieves reasonable performance with much lowercomplexity. Linear precoding strategies include maximum ratiotransmission (MRT), zero-forcing (ZF) precoding, and transmit Wienerprecoding. There are also precoding strategies tailored for low-ratefeedback of CSI, for example random beamforming. Nonlinear precoding isdesigned based on the concept of dirty paper coding (DPC), which showsthat any known interference at the transmitter can be subtracted withoutthe penalty of radio resources if the optimal precoding scheme can beapplied on the transmit signal. In DPC, only the transmitter needs toknow this interference, but full CSI is required everywhere to achievethe weighted sum capacity. This category includes Costa precoding,Tomlinson-Harashima precoding, and the vector perturbation technique.

While performance maximization has a clear interpretation inpoint-to-point MIMO, a multi-user system cannot simultaneously maximizethe performance for all users. This can be viewed as a multi-objectiveoptimization problem where each objective corresponds to maximization ofthe capacity of one of the users. The usual way to simplify this problemis to select a system utility function; for example, the weighted sumcapacity where the weights correspond to the system's subjective userpriorities.

In some aspects, the precoding weights for each user can be selected tomaximize a ratio between the signal gain at this user and theinterference generated at other users (with some weights) plus noise.Thus, precoding can be interpreted as finding the optimal balancebetween achieving strong signal gain and limiting inter-userinterference. Finding the optimal weighted MMSE precoding can bedifficult, leading to approximation approaches where the weights areselected heuristically. In such aspects, swarm intelligence may beemployed as part of the weight selection.

One approximation approach is MRT, which only maximizes the signal gainat the intended user. MRT is close to optimal in noise-limited systems,in which inter-user interference is negligible compared to the noise. ZFprecoding aims at nulling the inter-user interference, at the expense oflosing some signal gain. ZF precoding can achieve performance close tothe sum capacity when the number of users is large or the system isinterference-limited (i.e., the noise is weak compared to theinterference). A balance between MRT and ZF is obtained by the so-calledregularized zero-forcing (also known assignal-to-leakage-and-interference ratio (SLNR) beamforming and transmitWiener filtering) All of these heuristic approaches can be applied toreceivers that have multiple antennas.

In practice, the CSI is limited at the transmitter due to estimationerrors and quantization. If the complete channel knowledge is fed backwith good accuracy, then one can use strategies designed for having fullchannel knowledge with minor performance degradation. Quantization andfeedback of CSI is based on vector quantization, and codebooks based onGrassmannian line packing have shown good performance. In spatiallycorrelated environments, the long-term channel statistics can becombined with low-rate feedback to perform multi-user precoding. Asspatially correlated statistics contain much directional information, itis only necessary for users to feed back their current channel gain toachieve reasonable channel knowledge.

Beamforming algorithms require access to CSI, upon which the calculationof the optimal beamforming weights is based. The optimality criterion todetermine these weights can sometimes involve minimizing the totaltransmitted power at the relays, subject to satisfying desirable SINRlevels at all destinations. Centralized optimization methods, whichinvolve a central processing unit that collects the global CSI and thentransmits the optimal weights to all beamformers, incur a largecommunication cost. They can also entail significant delays, giving riseto the need for distributed techniques wherein each beamformer mustcalculate its optimal weights based on local information only.

Typically, the greatest overhead in distributed beamforming is thesharing of locations or CSI between the nodes to allow for weightcalculations across the network. While methods for calculating theseweights in a distributed fashion try to share as little data asnecessary, aspects of the disclosure can exploit the large databandwidth of local-area communications between UAV relay nodes. UWB,optical communications, and other WLAN and WPAN technologies can permitlarge volumes of data to be shared by cooperating nodes.

Starting with an initial estimate of ideal weights, the individual nodescan continue to refine their own weights locally using only a parameterbased on the combination of the transmissions in the uplink, which isfed back from the receiver. In one aspect, the second-order statisticcalculation includes multiple source-transmitter pairs, adjusting theweights at the relay nodes to optimize the signal at several receiversrather than just one.

Some aspects can employ a distributed optimization algorithm whichallows for autonomous computation of the beamforming decisions by eachcluster while taking into account intra- and inter-cluster interferenceeffects. Various algorithms may be employed in different aspects,including (but not limited to): an augmented Lagrangians (AL), which isa regularization technique that is obtained by adding a quadraticpenalty term to the ordinary Lagrangian. AL methods converge very fast,especially compared to first order methods; dual decomposition methods,which have the decomposability properties of the ordinary Lagrangian;Accelerated Distributed Augmented Lagrangians (ADAL) algorithms, whichare an AL decomposition method that produces very fast convergencerates; alternative AL decomposition techniques for general convexoptimization problems; other distributed methods utilizing dualdecomposition for the multi-cell downlink beamforming problem; and anAlternating Directions Method of Multipliers (ADMM) technique.

Some aspects can be adapted to the problem of cooperative beamforming inrelay networks where multiple clusters of source-destination node pairs,along with their dedicated relays, coexist in space. Utilizing theSemi-definite Relaxation technique, the problem can be approximated inconvex programming form and solved using a distributed optimizationalgorithm that allows for autonomous computation of the optimalbeamforming decisions by each cluster. This approach can combine lowcomputational complexity with the robustness and convergence speed ofregularization while requiring minimal communication overhead.

It should be appreciated that apparatus and method features disclosedherein might be implemented using any suitable combination of computingdevices, including servers, interfaces, systems, databases, agents,peers, engines, controllers, modules, or other types of computingdevices operating individually or collectively. One should appreciatethat computing devices comprise a processor configured to executesoftware instructions stored on a tangible, non-transitory computerreadable storage medium (e.g., hard drive, FPGA, PLA, solid state drive,RAM, flash, ROM, etc.). The software instructions configure or programthe computing device to provide the roles, responsibilities, or otherfunctionality as discussed herein with respect to the disclosedapparatus and method aspects.

The invention claimed is:
 1. An apparatus, comprising: a Radio AccessNetwork (RAN)-signal processor communicatively coupled to a plurality ofUnmanned Aerial Vehicles (UAVs) equipped with onboard radiotransceivers, the onboard radio transceivers configured to receive RANsignals transmitted by at least one of a controller and aremote-controlled vehicle, the RAN-signal processor producing RANperformance criteria from the RAN signals; a Threat Detection Systemconfigured to generate threat-detection data; and a Fleet Manageremploying the RAN performance criteria to coordinate flight navigationof the plurality of UAVs, thereby providing for improved reception ofthe RAN signals by the onboard radio transceivers.
 2. The apparatus ofclaim 1, wherein at least one of the RAN-signal processor and the radiotransceivers comprises at least one Channel State Information (CSI)estimator configured to generate RAN performance criteria correspondingto one or more targets to be attacked.
 3. The apparatus of claim 2,wherein the at least one CSI estimator is configured to characterize atleast one RAN channel between at least one of the plurality of UAVs andthe one or more targets from at least one of CSI, received signalstrength, bit error rate, transmission control messages, errordetection, and error control messages.
 4. The apparatus of claim 1,wherein at least one of the Threat Detection System and the FleetManager is configured to coordinate the plurality of UAVs to perform atleast one of an active attack and a passive attack on the at least oneof the controller and the remote-controlled vehicle.
 5. The apparatus ofclaim 1, wherein the RAN-signal processor comprises a mitigation systemconfigured to perform at least one of a passive attack and an activeattack on the at least one of the controller and the remote-controlledvehicle.
 6. The apparatus of claim 1, wherein the Fleet Managercomprises a scheduler configured to schedule particular ones of theplurality of UAVs for at least one of detecting the RAN signals andperforming countermeasures.
 7. The apparatus of claim 1, wherein theRAN-signal processor is configured to generate RAN mitigationperformance criteria.
 8. The apparatus of claim 1, wherein at least oneof the RAN-signal processor, the Threat Detection System, and the FleetManager resides in a central coordinator.
 9. The apparatus of claim 1,wherein at least one of the RAN-signal processor, the Threat DetectionSystem, and the Fleet Manager is distributed across multiple UAVs. 10.The apparatus of claim 1, wherein each of the plurality of UAVscomprises a flight controller configured to provide for autonomousflight and adapt flight rules based on RAN measurements.
 11. Theapparatus of claim 1, further comprising a fronthaul network managerconfigured to formulate a network topology comprising the plurality ofUAVs and adapt UAV flight to provide for communication links thatoperate within predetermined performance criteria.
 12. The apparatus ofclaim 1, further comprising a situational awareness manager configuredto communicate at least one of UAV flight telemetry data, remote sensingdata, and UAV position data to the fleet manager.
 13. The apparatus ofclaim 1, further comprising a synchronization manager configured tosynchronize signal processing operations onboard the plurality of UAVs.14. A method, comprising: employing a plurality of Unmanned AerialVehicles (UAVs) equipped with onboard radio transceivers to receiveRadio Access Network (RAN) signals transmitted by at least one of acontroller and a remote-controlled vehicle; producing RAN performancecriteria from the RAN signals; generating threat-detection data; andemploying the RAN performance criteria to coordinate flight navigationof the plurality of UAVs, thereby providing for improved reception ofthe RAN signals by the onboard radio transceivers.
 15. The method ofclaim 14, wherein the RAN performance criteria comprises Channel StateInformation (CSI) corresponding to one or more targets to be attacked.16. The method of claim 15, wherein the CSI characterizes at least oneRAN channel between at least one of the plurality of UAVs and the one ormore targets and is based on at least one of received signal strength,bit error rate, transmission control messages, error detection, anderror control messages.
 17. The method of claim 14, further comprisingcoordinating the plurality of UAVs to perform at least one of an activeattack and a passive attack on the at least one of the controller andthe remote-controlled vehicle.
 18. The method of claim 14, furthercomprising scheduling particular ones of the plurality of UAVs for atleast one of detecting the RAN signals and performing countermeasures.19. The method of claim 14, wherein at least one of producing,generating, and coordinating is performed by a central coordinator. 20.The method of claim 14, wherein at least one of producing, generating,and coordinating is distributed across multiple UAVs.
 21. The method ofclaim 14, wherein each of the plurality of UAVs comprises a flightcontroller configured to provide for autonomous flight and adapt flightrules based on RAN measurements.
 22. The method of claim 14, furthercomprising formulating a network topology comprising the plurality ofUAVs and adapting UAV flight to provide for communication links thatoperate within predetermined performance criteria.
 23. The method ofclaim 14, further comprising communicating at least one of UAV flighttelemetry data, remote sensing data, and UAV position data to a fleetmanager.
 24. The method of claim 14, further comprising synchronizingsignal processing operations onboard the plurality of UAVs.
 25. Anon-transitory computer-readable memory including a set of instructionsstored therein and configured to perform the method of claim
 14. 26. Anapparatus, comprising: a processor; and a non-transitorycomputer-readable memory coupled to the processor, the memory includinga set of instructions stored therein and executable by the processorfor: producing Radio Access Network (RAN) performance criteria from RANsignals received by a plurality of Unmanned Aerial Vehicles (UAVs), theplurality of UAVs equipped with onboard radio transceivers to receivethe RAN signals transmitted by at least one of a controller and aremote-controlled vehicle; generating threat-detection data; andemploying the RAN performance criteria to coordinate flight navigationof the plurality of UAVs, thereby providing for improved reception ofthe RAN signals by the onboard radio transceivers.
 27. The apparatus ofclaim 26, wherein the RAN performance criteria comprises Channel StateInformation (CSI) corresponding to one or more targets to be attacked.28. The apparatus of claim 26, wherein the CSI characterizes at leastone RAN channel between at least one of the plurality of UAVs and theone or more targets and is based on at least one of received signalstrength, bit error rate, transmission control messages, errordetection, and error control messages.
 29. The apparatus of claim 26,wherein the memory further includes a set of instructions stored thereinand executable by the processor for directing the plurality of UAVs toperform at least one of an active attack and a passive attack on the atleast one of the controller and the remote-controlled vehicle.
 30. Theapparatus of claim 26, wherein the memory further includes a set ofinstructions stored therein and executable by the processor forscheduling particular ones of the plurality of UAVs for at least one ofdetecting the RAN signals and performing countermeasures.
 31. Theapparatus of claim 26, wherein at least one of producing, generating,and coordinating is performed by a central coordinator.
 32. Theapparatus of claim 26, wherein at least one of producing, generating,and coordinating is distributed across multiple UAVs.
 33. The apparatusof claim 26, wherein each of the plurality of UAVs comprises a flightcontroller configured to provide for autonomous flight and adapt flightrules based on RAN measurements.
 34. The apparatus of claim 26, whereinthe memory further includes a set of instructions stored therein andexecutable by the processor for formulating a network topologycomprising the plurality of UAVs and adapting UAV flight to provide forcommunication links that operate within predetermined performancecriteria.
 35. The apparatus of claim 26, wherein the memory furtherincludes a set of instructions stored therein and executable by theprocessor for communicating at least one of UAV flight telemetry data,remote sensing data, and UAV position data to a fleet manager.
 36. Theapparatus of claim 26, wherein the memory further includes a set ofinstructions stored therein and executable by the processor forsynchronizing signal processing operations onboard the plurality ofUAVs.